QUANTUM MACHINE LEARNING WITH QISKIT AND CIRQ: Hands-On Projects Integrating Quantum Computing and Machine Learning
Format:
Paperback
En stock
0.33 kg
Sí
Nuevo
Amazon
USA
- Step into the future of intelligent computation with Quantum Machine Learning with Qiskit and Cirq, a comprehensive guide that bridges the worlds of quantum computing and artificial intelligence. This book offers a powerful blend of theory, hands-on coding, and practical insight, equipping readers to design, simulate, and execute quantum algorithms for real-world machine learning applications. Through clear explanations, code examples, and guided projects using Qiskit (IBM) and Cirq (Google), you’ll gain the skills to explore how quantum mechanics can revolutionize data processing, optimization, and predictive modeling. From the fundamentals of qubits and superposition to advanced topics like variational quantum algorithms (VQAs), quantum neural networks (QNNs), and hybrid quantum–classical systems, this book provides a structured path toward mastering one of the most transformative technologies of the 21st century. You’ll also learn how to set up quantum environments, run algorithms on near-term NISQ devices, and interpret real quantum results with clarity and confidence. Whether you’re a researcher, data scientist, software engineer, or an AI enthusiast eager to future-proof your skillset, this book will guide you through the intersection of quantum theory and machine learning practice—turning abstract principles into tangible breakthroughs. Inside You’ll Discover:The core principles of quantum computing and quantum information theoryThe architecture and workflow of Qiskit and CirqHow to implement quantum-enhanced ML models and kernelsStep-by-step setup guides for IBM Quantum and Google Cloud QuantumPractical exercises on quantum data encoding, error mitigation, and variational circuitsA complete appendix and glossary for quick reference and technical clarity Harness the convergence of AI and quantum physics—and unlock the computational possibilities of tomorrow, today.
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